Cheryl E1 English or E2 Chinese character separation

Import data from E1melted.Rdata

Warning: package 'dplyr' was built under R version 3.4.1

Will analyze data broken down by the following variables

condtnVariableNames = c('subject','wordEccentricity','side')

Fit mixture model with R (if fitting not previously done and cached).

# A tibble: 6 x 8
# Groups:   subject, wordEccentricity, side [6]
  subject wordEccentricity  side  efficacy     latency  precision      val
    <chr>            <int> <chr>     <dbl>       <dbl>      <dbl>    <dbl>
1      12                1  left 0.7061076  0.17926097 0.83105788 235.2828
2      12                1 right 0.4451992  0.05399706 0.55682248 283.2467
3      12                6  left 0.3346187 -0.15157202 0.88002418 302.2709
4      12                6 right 0.3838006  0.11302665 1.34039255 312.1915
5      13                1  left 0.7964935 -0.01168280 0.59205908 185.1992
6      13                1 right 0.2092368  0.48584419 0.03273432 323.1023
# ... with 1 more variables: warnings <fctr>

Plot some of the fits

• yellow = guessing component
• light blue = Gaussian component
• green = sum of the guessing and Gaussian components. In other words, the histogram heights predicted by the model
• dark blue = continuous Gaussian. This helps get a sense of the effect of discretising the Gaussian. For instance, it’s possible (especially using Pat’s method, it seems), for the Gaussian peak to fly high above the bars and still fit the discrete bins (or bin centers, in Pat’s method), suggesting an undesirably high estimates of the efficacy (likely accompanied by an undesirably low precision)

Warning: Removed 75 rows containing non-finite values (stat_bin).